An Anishinaabe Research Methodology that Utilizes Indigenous Intelligence as a Conceptual Framework Exploring Humanity’s Relationship to N’bi (Water)
Bibliographic record
Abstract
This article presents the utilization of an Anishinaabek Research Paradigm (ARP) that employs Indigenous Intelligence as a conceptual framework for qualitative Anishinaabek analysis of data. The main objective of the research project examines critical insights into Anishinaabek’s relationships to N’bi (water), N’bi governance, reconciliation, Anishinaabek law, and Nokomis Giizis with predominately Anishinaabek kweok, grassroots peoples, mishoomsinaanik (grandfathers), gookmisnaanik (grandmothers), and traditional knowledge holders. Drawing on Anishinaabek protocols, the enlistment of participants moved beyond the University requirements for ethics. This also includes “standing with” the participants in the act of inquiry, in knowledge, and continued relationships. The ARP for research emerged from Indigenous ways of seeing, relating, thinking, and being. This approach did not call for an integration of two knowledge systems but rather recognizes there are multiple ways of gathering knowledge. The article explains how “meaning-making” involves Indigenous Intelligence through Anishinaabek protocols holding the researcher accountable to the participants, the lands, the ancestors, and to those yet to come.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".